Computer Vision
Computer vision is a subfield of AI that enables machines to analyze, interpret, and understand visual data such as images and videos. It aims to replicate — and often surpass — human vision capabilities.
Key applications
- Medical imaging: assisting doctors in diagnosis.
- Surveillance and security: monitoring environments through cameras.
- Retail: cashier-less checkouts powered by object recognition.
- Augmented reality (AR): overlaying virtual objects onto real-world scenes.
- Autonomous driving: lane detection, obstacle recognition, traffic sign analysis.
Techniques
- Convolutional Neural Networks (CNNs) for feature extraction.
- Transfer learning with pre-trained models (e.g., ResNet, EfficientNet).
- Object detection frameworks like YOLO, Faster R-CNN, and Mask R-CNN.
Computer vision has become one of the flagship applications of AI, bridging the gap between how humans perceive the world and how machines process it. At its core, it transforms pixels into structured information that can trigger actions or decisions. What makes computer vision particularly powerful is its adaptability—from static image classification to dynamic video analysis.
Modern advances rely heavily on deep convolutional neural networks (CNNs), which can automatically learn hierarchical features: edges, textures, objects, and even abstract concepts. More recent architectures, such as Vision Transformers (ViTs), are reshaping the field by enabling models to capture global context more efficiently.
Real-world deployments illustrate both opportunities and challenges. In autonomous driving, vision systems must process streams of data with millisecond precision to ensure safety. In retail, cameras equipped with vision algorithms enable cashierless stores. But these applications also raise privacy and ethical concerns, particularly when surveillance becomes widespread or when datasets fail to represent diverse populations fairly.
Reference
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
